{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "## import requests\n",
    "import os\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (4, 4)\n",
    "plt.rcParams['figure.dpi'] = 150\n",
    "plt.rcParams['lines.linewidth'] = 3\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On this homework, we'll be looking at the Titanic dataset, which records the survival status of the passengers of the ship \"Titanic\", which sunk on its first voyage, in 1912."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df = sns.load_dataset(\"titanic\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  \n",
       "0    man        True  NaN  Southampton    no  False  \n",
       "1  woman       False    C    Cherbourg   yes  False  \n",
       "2  woman       False  NaN  Southampton   yes   True  \n",
       "3  woman       False    C  Southampton   yes  False  \n",
       "4    man        True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For example, the first passenger listed  was a male, age 22, who did not survive."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 1: One Dimensional Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this first part of the homework, we'll build a classifier that predicts whether or not someone survived based only on their age."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "p1a_df = df[[\"survived\", \"age\"]].copy()\n",
    "p1a_df = p1a_df.dropna()  # drop entries with missing data\n",
    "p1a_df = p1a_df.sort_values(\n",
    "    \"age\"\n",
    ")  # sort data by age, which will make it easier to plot the data nicely"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a plot of the data in `p1a_df` showing survival on the y-axis and age on the x-axis.\n",
    "\n",
    "You should add jitter to the y-axis to spread the points out. \n",
    "\n",
    "Does it look like older or younger people were more likely to survive?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'survived')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(\n",
    "    p1a_df['age'],\n",
    "    p1a_df['survived'] + np.random.uniform(-0.1, 0.1, len(p1a_df['survived'])),\n",
    "    'o')\n",
    "plt.xlabel('age')\n",
    "plt.ylabel('survived')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train a logistic regression model `p1a_model` that tries to predict \"survived\" from \"age\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p1a_model = LogisticRegression()\n",
    "p1a_model.fit(p1a_df[['age']], p1a_df['survived'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now create a plot showing the jittered data overlaid with the probability of survival predicted by your model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'survived')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(\n",
    "    p1a_df[['age']],\n",
    "    p1a_df['survived'] + np.random.uniform(-0.1, 0.1, len(p1a_df['survived'])),\n",
    "    'o')\n",
    "plt.plot(p1a_df[[\"age\"]], p1a_model.predict_proba(p1a_df[[\"age\"]])[:, 1])\n",
    "plt.xlabel('age')\n",
    "plt.ylabel('survived')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If done correctly, you should see that the probability is around 45% for 0 year olds and around 35% for a person who is 80.\n",
    "\n",
    "In other words, younger people tended to survive, but not by much."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** You might ask why we don't see the S-shaped curve. The reason is that we are too zoomed in. If we look at people between -300 and 300 years old, we'll see the S. Of course, in the real world, a negative age is impossible, but the mathematical model does indeed have the S, as seen in the figure below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x18eee4845c8>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iooDEHiE/wh6hK5PMZ2E/mAn7gYx6X5up41Og63Mz1Ik9rjn9nn+tkbv4zJA7+Lz4FnuEKCSojJHQp94GXe8JcOT9DNveHyBVFLvOiyU5MJfkQBXbCfrU26Bu20vxrTyIiMj/sUfIj7BHqOFkWYJ4fA+su1fXO45IFdMR+tRboW7X57JAxL/WyF18ZsgdfF58iz1CFJIEQQVN++uhbtcH4slc2HalQyza7zovlR2Fee0/oYrpBH2/adC06aZgtURE5K8YhCigCYIATXwyNPHJEEvzYd3xDcTCva7zUtkRmFe/AXVCV+hvnAp16yQFqyUiIn/DV2N+hK/GPEMsOwrrjlUQj++57Jym/fWIG/cAdK0S2W1NDcZXHeQOPi++xXWEggiDkGeJp/Jg/fWry1esFlSIuH40pG43QxXWXJniKKDwHzZyB58X32IQCiIMQt7hOPEbrL98CansSN0TWgN0vSdA12scBA03qqQr4z9s5A4+L77FIBREGIS8R5ZlOI5uh/WXFZDPltY5JzRrCf2Au6Dp2JdT7qle/IeN3MHnxbcYhIIIg5D3yaID+uObUZG9ApK5us45dXwK9DfdDXXLtgpVR/6K/7CRO/i8+FZTg5DKg7UQ+T1BrUHzfhPRdta/oO01DhAu/J9HLMmB6et5sGxZDtlmVrBKIiLyFQYhCklqQzgMA+5C+O2vQN2214UTsgz7/h9R88WzsOdvAztMiYiCG4MQhTRVVDzCxj8J47g5EJq3drXLpkpYMt6F+fu3IFWdVLBCIiLyJgYhIgCadr0RPvVl6PpOBtQX1hkVTxxAzZd/hXX3asiSQ8EKiYjIGxiEiM4TNDroU29D+O2v1X1dJtph+2UFTCvnQyw7plh9RETkeQxCRJdQRcbCOG4ODKNnQzBeWHBROnMcplUvwfLzfyE7bApWSEREnsIgRFQPQRCg7XQjwu94DdqUoRdOyDLse3+A6esXIZYeufIFiIgoIDAIEV2FoA+HYehDMN78ZwiRsa52qbIYpm9egfWXLyGLdgUrJCKipmAQImoATZtuCJ/2MrQ9xgA4v/q0LMG2+zuYVr4EsbxQ0fqIiKhxGISIGkjQ6GG4aTqMtzwDISLG1S6VF8G08iXY9q2DLEsKVkhERO5iECJykyY+2dk71G3khUbRAevWz2D+YQEkU6VyxRERkVsYhIgaQdAaYBh8H4zjn4RgjHS1i0X7YVoxF46C3QpWR0REDcUgRNQEmra9EDbtFWjaX+9qk63VMK/9Byxb/wNZ5CKMRET+jEGIqIlUxkgYxjwO/ZAHAI3O1W7ftxamb1+FdLZMueKIiOiqGISIPEAQBOi6DkfY5Behik50tUtlR1Hz9TzYj25XsDoiIroSBiEiD1JHJyBs8jxoU4ZfaLSZYflxESw/fw5ZEhWrjYiILscgRORhgkYHw9AHYBj5KKA1uNrte9fAvPoNziojIvIjDEJEXqJNGoDwKS9C1eLCqzKx5BBMX78Ix8lcBSsjIqJaDEJEXqRqHoew2/4KTdJAV5tsqoQ5/XXYfstUsDIiIgIYhIi8TtDqYRgxA/pB9wIqtbNRFmHN/gSWTZ9wij0RkYIYhIh8QBAE6LqPQtgtz0IIi3K12w9mOscNmc8qWB0RUehiECLyIXXrJIRNfgGq2E6uNvFkLkxfvwjxzHEFKyMiCk0MQkQ+pgqPRtjEZ6DpMsjVJteUw/TNq9yag4jIxxiEiBQgaHQwDHsY+oG/AwTB2eiwwrzun7DtWwtZlpUtkIgoRDAIESlEEAToeo6FceyfLqw3JMuwbv0PrNlLIUscRE1E5G0apQvwhKNHj2Lx4sXYsWMHzpw5g7i4OIwfPx4zZ85EWFiYW9favXs33n//fezcuRM1NTVo2bIlbrrpJvzhD39Au3btvPQbUCjTtOuNsNueh3nNPyBXnwHgHEQtVZ+GcfRsCBctykhERJ4lyAHeB793717cf//9MJlM6NWrF+Lj47Fz506UlZUhOTkZn332GZo1a9aga/3www946qmnIIoiunfvjoSEBBw6dAjHjx9HeHg4lixZgl69enntd7HbRVRWmrx2fXKKiYkAAJSVnVO4krokUyXM696GVHrE1aZq1R7GcXOgumimGfmevz4z5J/4vPhWVFQYtFp1o38+oIOQw+HAuHHjUFhYiFdffRXTpk0DAFgsFsyZMwcbNmzA3XffjXnz5l3zWmazGUOHDsW5c+fw1ltvYeLEiQAAURTxxhtvYMmSJejSpQvS09O99vswCPmGP39JyQ4bLJkfwHHRJq1CRCuEjX8Kqqh4BSsLbf78zJD/4fPiW00NQgE9Rmj16tUoLCzEwIEDXSEIAAwGA1577TWEhYXhiy++QFVV1TWvtXv3bpw9exadO3d2hSAAUKvVmDNnDtRqNXJzc1FeXu6V34UIOD+IevQsaHukudrkc6dR880rEE8eVrAyIqLgFNBBaMOGDQCAtLS0y85FR0ejf//+sNvt2LRp0zWvpVY702R5eTlsNludcxUVFRBFEVqttsGv2YgaSxBUMNx0N/QDfneh0VoD0+o34Ti+R7nCiIiCUEAHodxc58aVycnJ9Z5PSkoCAOTk5FzzWj179kSLFi1w+vRpPPXUUzhy5AgsFgv27NmD2bNnAwAeeOAB6HQ6D1VPdHW6XmNhGDULUJ2f0yDaYF77NuyHtyhbGBFREAnoWWOlpaUAgNatW9d7PjY2ts7nrsZoNGLRokWYM2cO1q1bh3Xr1rnOGQwGzJ8/H3feeacHqiZqOO11/SAYI2Be+0/AbgFkEZbMDyBba6DrcXlPKBERuSegg5DJ5BxYbDDUP724tr32c9fSsWNH3Hrrrfi///s/pKSkIC4uDrm5uSgsLMSSJUvQrVs39OzZ0zPF10OrVbsG2ZH3Bcz/1jH9YI19GSWfvwzJ5NyTzLplOYwqG6KH3gmhdkFG8rqAeWbIL/B5CQwBHYTUajUkSbrmPwQNmRhXUlKCe+65BxUVFfj4448xcOBA189+8skn+Nvf/oYHH3wQ6enpiI/n7B3yLX18JyTc9ypOfvYSHGdPAwAqs1dAtpnRYvQDDENERI0U0EEoPDwclZWVMJvN9Z63WCwAnK+9rmXhwoUoKirCs88+6wpBgHP13wceeAD79+9Heno6lixZgmeffdYzv8AlOH3eNwJ3amsE9BOfg/T9W5AqiwEAVb98B9O5GugH3wtBCOghf34tcJ8ZUgKfF98K6enztWOAysrK6j1fOzao9nNXs2WLcwDqkCFD6j0/fPhwAMD+/fvdLZPIY1TNWsB467NQtWrvarMfzIQl62PIkqhgZUREgSmgg1DtbLG8vLx6z9e2X2lW2cVq1xrSaOrvJKudXu9wcP8nUpbKEIGwm/8MVeskV5vj8GZYNrwHWeTzSUTkjoAOQsOGDQMArF279rJzFRUV2LZtG7RaLQYNGnTNa1133XUALqxNdKns7GwAQNeuXRtbLpHHCPpwhE14Gur4FFeb48ivsGT8i2GIiMgNAR2E0tLSkJCQgOzsbCxfvtzVbrFY8Pzzz8NkMmHatGlo1aqV65zdbkd+fj7y8/Nht9td7XfffTcAYNGiRfj111/r3OfLL7/EV199Ba1Wi3vuucfLvxVRwwhaA4zjn4S67YWZjI5jO2FZv5hhiIiogQJ6rzEA2LZtG2bMmAGLxYLu3bsjMTERu3btQmlpKbp164ZPP/20zmrQRUVFGDVqFAAgIyMDiYmJrnPz5s3Df//7XwDOBRbj4uKQl5eHo0ePQqvV4tVXX8Vtt93mtd+Fg6V9I9gGMsqiHeYfF0M8vtvVpml/PQyjZ0NQB/R8CL8RbM8MeRefF98K6cHSANC/f3+sWLECY8eORXFxMbKyshAREYFZs2ZdFoKuZf78+Vi8eDEGDx6MwsJCZGZmorq6GhMnTsSKFSu8GoKIGktQa2FMmw11uz6uNkfBLvYMERE1QMD3CAUT9gj5RrD+tSaLDljWL4ajYJerTdP+ehjSZkNQsWeoKYL1mSHv4PPiWyHfI0REToJaA8Po2dC0v97V5ijYBcuGDzi1nojoChiEiIJIbRiq85rsyC+w/PQxZFlSsDIiIv/EIEQUZAS1xjlmKLGHq81xeAusmz5p0HYzREShhEGIKAgJai2MY/4IdcKFda/sOT/BumU5wxAR0UUYhIiClKDRwzj2Cahbd3a12Q+sh23HSgWrIiLyLwxCREGsdtFFVUxHV5tt57ew7V2jYFVERP6DQYgoyAk6I8LGPwVVdBtXm/Xnz2HP2ahgVURE/oFBiCgECIZmME54GkJEjKvNsunfsB/59So/RUQU/BiEiEKEKjwaYTf/D4SwKGeDLMOy4T04ig4oWxgRkYIYhIhCiCoyFsYJTwP6cGeDJML84zsQTx9TtC4iIqUwCBGFGHWLRISNfxLQ6JwNdgvMPyyAdLZU2cKIiBTAIEQUgtSx18E4+jFAcH4FyOazMK1+E5KpSuHKiIh8i0GIKERp2vWCYdjvXcfyuTKYf1gA2WZWsCoiIt9iECIKYdoug6Dvf4frWDpTAPP6xZAlh4JVERH5DoMQUYjT9hoPbc+xrmOxaD8sG7kvGRGFBgYhohAnCAL0A+6EplM/V5sjdxNsO79VsCoiIt9gECIiCIIKhuEPQx3XxdVm27ES9txsBasiIvI+BiEiAgAIGh2MYx6Hqnmcq83y07+54CIRBTUGISJyEQzNYBz/JARjpLNBFmFevwhixQllCyMi8hIGISKqQxUZC+PYP11YcNFmhnnNPyCZzypbGBGRFzAIEdFl1LGdYBg5E4AA4PwaQ+vehuywKVsYEZGHMQgRUb20HW6Avv/trmPpVB4sG/+P0+qJKKgwCBHRFWl7jYc2Zajr2JH3M2w7v1GwIiIiz2IQIqIrEgQB+sH3QZ3Q1dVm27EK9vxfFKyKiMhzGISI6KoElQbGtMfqTqvP+gji6WPKFUVE5CEMQkR0TYI+HMZxfwL04c4G0Qbz2rchmSqVLYyIqIkYhIioQVTN42AcNQsQnF8bck05Z5IRUcBjECKiBtMkdod+4HTXsVR6BJZNSziTjIgCFoMQEblF230UtF2Hu44dh7fAvneNcgURETUBgxARuUUQBOgH3QN1fIqrzfrLF3AU7VewKiKixmEQIiK3CSoNDKNnQWjW0tkgyzBnvAvpbKmyhRERuYlBiIgaRWWMhHHM44D6/J5k1hrn4Gm7RdnCiIjcwCBERI2mbtUehmEPuY6l8iJYsj7i4GkiChgMQkTUJNqkAdD2Gu86dhzdDtue1QpWRETUcAxCRNRk+n63Q53Yw3Vs+/UrOIoOKFgREVHDMAgRUZMJKhWMIx+FEBHjbJBlWDLehXTutLKFERFdA4MQEXmEYGgGY9pjgFoLAJCt1TD/uIgrTxORX2MQIiKPUbdqD8OQB1zH0uljsG5eplxBRETXwCBERB6l7TII2m4jXcf2QxthO5ilXEFERFfBIEREHqcfOB2q1kmuY+vmZRDLjilXEBHRFTAIEZHHCWoNjKNnQzBGOhskB8zrF0G21ihbGBHRJRiEiMgrVOHRMIz6AyAIAAD53GmYMz+ALEsKV0ZEdAGDEBF5jSahK3R9p7qOxeN7YNvzvYIVERHVxSBERF6l6zMB6na9Xce2X7+Co/igghUREV3AIEREXiUIKhhHzIAQ0crZULvYoqlS2cKIiMAgREQ+IOjDYRz9GKDSAABk81lYMt6DLHG8EBEpi0GIiHxCHdMB+pumu47FkhzYdq5SsCIiIgYhIvIhbdcR0FzX33Vs25kOR9F+BSsiolDHIEREPiMIAgxDHoDQvPX5FhmWDe9DqqlQtC4iCl0MQkTkU4LOCOPo2Rc2Z7Wcg2XDe5AlUeHKiCgUMQgRkc+pW7aD/qa7XcdiySHYdnC8EBH5HoMQESlCmzIMmqSBrmPbru/gOPGbghURUShiECIiRQiCAMPg+y4fL2Q+q2hdRBRaGISISDGCzgjjqFkXrS9UBQv3IyMiH2IQIiJFqVu1h37Ana5jsWg/bHvWKFgREYUSBiEiUpy2+2hoOqS6jm2/fgXxVJ6CFRFRqGAQIiLFCYIAw9CHIDRr6WyQRZgz3oVsrVG2MCIKegxCROQXBEMzGEc+CgjOr3D12qsAACAASURBVCW5+gwsmz6BLMsKV0ZEwSwogtDRo0fx9NNPY8SIEejVqxfGjBmDhQsXwmQyuX0tk8mERYsW4ZZbbkHv3r1x/fXXY/r06VizhmMWiLxNHdcZuhsmuY4dR36BIzdbwYqIKNgFfBDau3cvpkyZgvT0dLRq1QrDhw+HyWTCe++9h7vuugvV1dUNvtbp06dx++2345133kF5eTkGDx6M5ORk7Ny5E0888QSWLFnivV+EiAAAuj4ToY5PcR1bNn8KqbJEwYqIKJgJchP6nb/88kuPFDFt2rRG/ZzD4cC4ceNQWFiIV1991XUdi8WCOXPmYMOGDbj77rsxb968Bl1v5syZyMrKwtixY/Hmm29Cr9cDALZs2YIZM2ZAkiSsX78eCQkJjar3Wux2EZWV7vdikXtiYiIAAGVl5xSuhK5EqqlAzZdzgfNjhFQt2yFs0l8hnN+Ww9f4zJA7+Lz4VlRUGLRadaN/vklBKCUlBYIgNPrmtQ4ePNion/vmm2/w5z//GQMHDryst6aiogIjR46E3W7H5s2b0bx586tea+/evbj99tvRrl07fPfdd64QVGvevHnIzs7GM888gzFjxjSq3mthEPINfkkFBvuxnbCse9t1rO2RBsNF23L4Ep8ZcgefF99qahDSNOXmV+oZsVgsKC8vBwBEREQgKSkJzZs3h8ViQV5eHk6fPg1BEJCSkoKoqKhG33/Dhg0AgLS0tMvORUdHo3///sjMzMSmTZswceLEq17rhx9+AADce++9l4UgAJg/f36j6yQi92k7pELsNgr23zIAAPb9P0KT2BOadr0UroyIgkmTglBtELlYdXU1fve738Fms2Hu3LmYOHEiNBrNZT83d+5cVFdX4+OPP270/XNzcwEAycnJ9Z5PSkpCZmYmcnJyrhmE9u/fDwDo06cPTCYT1q5di3379kEURfTs2RMTJ06EwWBodK1E5D79gDshnjwEqbwIAGD56SOETX0ZqrCr9/ASETWUxwdLL168GHl5eVi4cCEmTZp0WQgCgJEjR+Ltt99GYWEhFi5c2Oh7lZaWAgBat25d7/nY2Ng6n7uaY8eOAXC+Ups4cSKeeeYZLF++HJ9//jmef/553HzzzTh8+HCjayUi9wkaHQwjHwXUtVtwnIXlp485pZ6IPKZJPUL1WbNmDRITEzFkyJCrfq5v377o0KEDMjMzG32v2unxV+qpqW1vyDT62tllTz/9NBISErBs2TJ07doVRUVFeOutt7Bp0yY88sgj+O6779CsWbNG13w1Wq3a9W6ZvI//WweImK6oGnU/zqxz9h6LhXuhL8hG8xsn+L4UPjPkBj4vgcHjPULl5eWIjIxs0GeNRiNqahq/cqxa7Rwcda0B2w3569FqtQIAdDodli5dihtvvBHNmjVDSkoK3nvvPXTp0gUlJSX4/PPPG10vETVOZN/xCEu6wXVcnrEUttICBSsiomDh8R6huLg4HD58GBUVFYiOjr7i5woLC5Gbm4tOnTo1+l7h4eGorKyE2Wyu97zFYgHgDFzXYjAYUFNTg0mTJl02w0yj0eCuu+7C/PnzsXXrVjz88MONrvlqOGvMNzijIzAJA++HcOIwZPNZyKIdxV8uQNjkeRA0Oq/fm88MuYPPi281ddaYx3uERo4cCZvNhqeeegrnztX/EJSWluKJJ56AJEmYMKHx3du1Y4DKysqueJ+LP3c1rVq1AgAkJibWe762vaKiwu06iajpVMZIGIY/4jqWKopg/WWFghURUTDweI/QQw89hPT0dGzduhWjR4/GqFGj0KVLF4SFhaG6uhq//fYbNmzYAJPJhKSkJNx7772NvldycjJyc3ORl5eH1NTUy87n5eW5PteQaxUUFODUqVP1nq8NWy1atGh0vUTUNJq2PaHtORb2fWsBnJ9S37YXNG17KlwZEQUqj/cIxcTE4KOPPkKnTp1QVVWFlStX4vXXX8cLL7yAN998E9999x1MJhNSU1Px4YcfNmng8bBhwwAAa9euvexcRUUFtm3bBq1Wi0GDBl3zWsOHDwfgXE/Ibrdfdn7jxo0AgH79+jW6XiJqOv2NU6FqcaHn1pL1ESTzWQUrIqJA5pW9xlJSUvDtt99iwYIFuPXWW9GzZ0+0a9cOvXv3xuTJk/Huu+9i+fLliI+Pb9J90tLSkJCQgOzsbCxfvtzVbrFY8Pzzz8NkMmHatGmu114AYLfbkZ+fj/z8/DqBZ8KECUhMTMSxY8cwf/78OudWrFiBtWvXonnz5pg6dWqTaiaipnFOqZ950ZT6Klg3/ptT6omoUZq0xYY/2LZtG2bMmAGLxYLu3bsjMTERu3btQmlpKbp164ZPP/20Tq9TUVERRo0aBQDIyMioMyZo//79ePjhh1FRUYHY2Fj07t0bBQUFyM3NhV6vxz/+8Q+MHDnSa78LB0v7BgcyBgfbvrWwbv2P61g/5AHoug73yr34zJA7+Lz4lt8Nlr6ULMs4efKkaxXo2jZP6d+/P1asWIGxY8eiuLgYWVlZiIiIwKxZsy4LQdfSo0cPpKen495774VOp0NWVhYqKipw880344svvvBqCCIi92h7pEHdprvr2Lr1M0iVJxWsiIgCkdd6hPLy8vCvf/0LGzduRE1NDQRBwG+//YaSkhLcd999mDFjBm6//XZv3DpgsUfIN/jXWvCQaipg+vKvkK3OBVFVMZ0QdttzEFSenQfCZ4bcwefFt/yyR+jHH3/EtGnT8MMPP6C6uhqyLLt6gUpKSlBYWIh58+bh9ddf98btiShEqMKjoR/6oOtYKjsC2850BSsiokDj8SBUUFCA//mf/4HFYsG4cePw3nvvoVu3bq7znTp1wrRp0yDLMpYsWYKsrCxPl0BEIUTb8QZoky9s6WPblQ6xNF/BiogokHg8CH388cewWCx49NFHsXDhQgwfPrzOXmBRUVF45ZVX8Pjjj0OWZW5ZQURNph84HUJEjPNAlmDe8AFku0XZoogoIHg8CG3evBnNmjXDrFmzrvq5hx9+GJGRkdi3b5+nSyCiECPojDCOmAGc33dQPnsK1q38I4uIrs3jQai0tBQdOnSATnf1/X90Oh3atm2LqqoqT5dARCFIHdcZuj4TXcf2nCw4CnYpWBERBQKPB6GwsDCcPn26QZ+tqqpCeHi4p0sgohClu+E2qFp1cB1bNv6bq04T0VV5PAglJyfj1KlT2L9//1U/t2vXLhQVFTVoHzAiooYQVBoYRs4A1FoAgGw+y1WnieiqPB6EJk+eDFmW8dxzz11xV/gjR47g6aefhiAIuOWWWzxdAhGFMHVUAvT973QdOwp2wXFok4IVEZE/8/ju87fddhu+/fZbbN26FWPGjEH//v1RUFAAAHjjjTeQl5eHLVu2wOFwoE+fPpgyZYqnSyCiEKftPhKO47shFjl7pi1bP4M6IQWqyFiFKyMif+OVlaVNJhPmzp2L77//vu7NBMHVRT1kyBC8+eabiIqK8vTtAxZXlvYNrvoaGqSaCtR8ORew1gAA1K07w3jLsxBU7neE85khd/B58a2mrizt1U1Xc3JysH79euTm5qK6uhpGoxEdO3bEiBEjcMMNN3jrtgGLQcg3+CUVOuz522DJeNd1rOt3O/R9bnb7OnxmyB18XnyrqUHI46/GLpaSkoKUlBRv3oKI6Iq01/V3jhHK+xkAYNv+NTSJPaBu1V7hyojIX3h8sPTIkSOxaNEiFBYWevrSRERuMwy6F0J4C+eBJMKS+SFkh03ZoojIb3g8CBUXF2Px4sUYM2YM7rvvPqxatQpms9nTtyEiahBBHw7DsN+7jqWKIli3r1SwIiLyJ+oXX3zxRU9esF27djCZTDhx4gSKioqQkZGBpUuX4vjx42jevDkSEhI8ebugIkkyLBa70mUEvfBwPQDAZGKvQKhQRcZCtlRDKjsCAJBO5UOd0BWqiFYN+nk+M+QOPi++ZTBooVY3vl/Ha4OlT58+jfT0dKxatQqHDh1y3kwQ0LZtW0yZMgW33XYb4uPjvXHrgMXB0r7BgYyhSXZYYfrqBUhVJwEAQkQrhE99GYLOeM2f5TND7uDz4lt+PWus1uHDh7Fy5UqsXr0ap06dAgCoVCoMGDAAU6ZMwcSJE69xhdDAIOQb/JIKXWLpEZi+eQWQJQCANnkoDMMeuubP8Zkhd/B58a2ACEK1ZFnGtm3bsGbNGqSnp6OmpgYqlQq//fabr0rwawxCvsEvqdBm3b4Stp3fuI6NY56ApsP1V/0ZPjPkDj4vvtXUIOTxwdJXc+jQIfz888/Yvn07TCbnP/iqRixuRkTUWLrUW6CK6eg6tmzixqxEocyr6wgBzllk6enpSE9PR35+PgBnz1Dnzp0xdepU3Hrrrd4ugYjIRVBpYBjxCExfvQCIdufGrJs+gSHtMQiCoHR5RORjXglCVVVVrtdfO3fuhCzLkGUZkZGRuPnmmzF16lT06NHDG7cmIrom58asd8C6ZTkAwHFsBxyHt0DbZZDClRGRr3k8CM2ePRsbN26Ew+GALMtQqVS46aabMHXqVIwePRo6nc7TtyQicpu2+yg4ju2EWHwQAGDZvMy5MWuzlgpXRkS+5PEglJGRAcC5ntDkyZMxefJkxMXFefo2RERNIggqGIY/jJoVcwG7GbCbYfnpYxgnPA1B4NhFolDh8SA0efJkTJ06FX379vX0pYmIPErVrCUMg+6BJetDAIB44jfYD2RA1yNN4cqIyFc8/mfP3/72N4YgIgoYms43QdPhBtexddsKSJUlClZERL7UpB6h2o1VExISoFar67S5o23btk0pg4io0QRBgH7I/RBPHYZsPguINpgzP0TYbc9DUDV+bRIiCgxNCkJpaWlQqVRYvXo1OnZ0rssxZswYt64hCAIXVCQiRamMkdAPeQCWdW8DAKSyI7DtXg19Kpf3IAp2TX41JklSnePaqfIN/e/SnyciUoK2Qyo0XQa7jm07voF4ukDBiojIF5rUI1Q7Q6x169aXtRERBRrDTdNRU3wQcvUZQBZhyfwAYZNfULosIvKiJgWhNm3aNKiNiCgQCLowGIY/DPN3rwMApIoTsG7/GrjlEYUrIyJv8fissXvuuQcrV6507SVGRBRINAldoe1xYayjfe9amI9zHCNRsPJ4ENq+fTuee+45DBo0CM8++yx+/fVXT9+CiMir9P2mQRUVf/5IRln6O5CsZkVrIiLvUL/44osvevSCajVKS0tRVlaGnJwcrFq1CqtWrUJ1dTXatGmDiIgIT94uqEiSDIvFrnQZQS88XA8AMJlsCldC/kpQqaGO6QT7oU0AZEiWGoims0CbXkqXRgGA3zG+ZTBooVY3vl9HkGVZ9mA9Lvv27cM333yD77//HuXl5RAEAYIgoH///pgyZQrGjBkDvV7vjVsHLLtdRGUlXyl6W0yMM4yXlZ1TuBLyd9btK2Hb+Y3r2DjuT9C066NgRRQI+B3jW1FRYdBqG7/ml9eCUC1RFLFx40asWrUKWVlZsFqtEAQB4eHhmDBhAiZNmoTU1FRvlhAwGIR8g19S1FCy5IBp1SuQTh8DAAjGSITd/ipUBvZs05XxO8a3/D4IXay6uhpr167FmjVr8PPPP8PhcHBBxYswCPkGv6TIHWLFCZi/fhGy6HxtrenYF4bRsyEIgsKVkb/id4xvNTUI+XSLZZvNBkmSoFKpIAiCa1FFIiJ/pY5ug+gRd7uOHUe3w5G3VcGKiMiTPL77/KUsFgvWr1+P9PR0bN68GaIoQpZlREVF4Y477sDUqVO9XQIRUZM073czTId/haXgAADAsvlTqONToGrWQuHKiKipvBKEZFnG5s2b8e2332L9+vUwm82QZRlqtRpDhgzB1KlTMXLkSGi1Wm/cnojIowRBhZhbHkPh+3MAuwWwmWH56WMYJzwFQfBpxzoReZjHg9Brr72G77//HmfOnHG99urYsSMmT56MSZMmITY21tO3JCLyOm3zWBhuuhuWnz4GAIgnDsB+YAN0PUYrXBkRNYXHg9DSpUsBAOHh4Rg/fjymTJnCWWFEFBQ0XQZDc2wnHAW7AADWbV9Ak9j9osUXiSjQeDwIpaam4s4778TYsWNhMBg8fXkiIsUIggD90AchfpkP2XwWEG0wZ36IsNueh6Bq/KwVIlKOx19ua7VabNq0CTYbV9QkouCjMkZCP+QB17FUdgS2Xd8pVxARNYnHg9CBAwewbds2REZGevrSRER+QdshFZouQ1zHtp3fQCw7qmBFRNRYHg9CoiiiZcuWnr4sEZFfMdw0HUJEK+eBLMGS+QFkh1XZoojIbR4PQqNGjUJubi527Njh6UsTEfkNQWeEYfgjAJwrTEuVJbBuW6FsUUTkNo8Pln722WdRXFyMhx56COPHj8cNN9yAmJiYq26wOnDgQE+XQUTkdZr4ZGh7jYV97xoAgP3Aemja94EmsYfClRFRQ3l8r7GuXbu6VwD3GnPhXmO+wX2AyF1Xe2Zk0Q7TypcglRcBAITwaIRPewWCPtynNZL/4HeMb/ndXmO1+4c19D9JkjxdAhGRzwhqLQwjZgDnp8/LNRWwZC9VuCoiaiiPvxrLycnx9CWJiPyaumU76PpOge0X5xghR/422NtfD23SAIUrI6Jr4SY5REQeoOs1Huq4Lq5jS/YnkKrPKFgRETUEgxARkQcIKhUMIx4BtOdX1LeZYcn6CLLM1/9E/swrs8bcIQgCXnvtNU+XQUTkc6qImLobsxYfhH3fj9D1GqtwZUR0JR4PQitXroQgCKhvMpogCHWOZVlmECKioKLpMhiagt1wHHOupWb9dQXUid2hbpGocGVEVB+PB6FJkyZdFnhqmUwmlJaWYv/+/XA4HHjwwQfRqVMnT5dARKQY58asD0A8lQfZXAWIDlgy30fYpHkQ1FqlyyOiS3h8HaGGOHnyJGbNmoWSkhJ8++23iImJ8XUJfonrCPkG1/ggdzXmmXEc3wvzmgWuY22vcTAMuMvjtZH/4XeMb/ndOkINERcXhwULFqCqqgqLFi1SogQiIq/StOsFbbdRrmP73rVwnODisUT+RrFZYx06dECnTp3w008/KVUCEZFX6QfcAVVU/Pkj2TmLzFqjaE1EVJfi0+fPnGn6OhtHjx7F008/jREjRqBXr14YM2YMFi5cCJOp6a+ZXn/9dSQnJ+Odd95p8rWIKLQIGj0MI2detOp0OSybPql3MgkRKUOxILRz507k5eU1eXzQ3r17MWXKFKSnp6NVq1YYPnw4TCYT3nvvPdx1112orq5u9LU3b96Mf//7302qj4hCm7pVB+j6TnEdO478AkfeVgUrIqKLeXzW2JdffnnFc7Isw2az4ejRo65p9sOGDWv0vRwOB5588kmYTCa8+uqrmDZtGgDAYrFgzpw52LBhAxYsWIB58+a5fe3y8nL85S9/4V9uRNRkul7jIRbuhVhyCABgyf4U6tadoYrkRBEipXk8CM2dO/eK0+cvJssyWrVqhUcffbTR91q9ejUKCwsxcOBAVwgCAIPBgNdeew0jR47EF198gSeeeALNmzd369rPPfccKioqkJqaip07dza6RiIi56rTM1Dz5VzAZgbsZlgyP4DxlmcgqBo/24WIms7jr8YSEhIQHx9/xf/atm2LXr164cEHH8RXX32F1q1bN/peGzZsAACkpaVddi46Ohr9+/eH3W7Hpk2b3Lru8uXLkZmZidmzZ6NHjx6Nro+IqJaqWUsYBt/vOhZPHYZt13cKVkREgBd6hGrDiS/k5uYCAJKTk+s9n5SUhMzMTOTk5GDixIkNuubhw4fx+uuvIzU1FTNnzsTf//53j9VLRKFNmzQAjsK9cBzeAgCw7fwGmsTuULdOUrgyotDl08HSp06dwqeffoolS5YgPz+/ydcrLS0FgCv2KsXGxtb53LVYrVY8+eST0Gq1ePPNN6FWs8uaiDzLMOheCBHnxwbJEswb3odsMytbFFEI83iPEADk5OTgf//3f5GUlIS//OUvAIAdO3bg4YcfhsViAQC88cYbeOqpp/D73/++0fepnR5vMBjqPV/b3tBp9G+88QZyc3Px+uuvIzHR9/sCabVq14qk5H3835rc5ZlnJgKWKXNQvHQuIEuQz5UBO/6LmFv/6IFrkz/hd0xg8HiP0IkTJ3DPPfcgOzu7Tq/PSy+9BLPZjPDwcCQlJUGSJLz11lvYvXt3o+9V22NzrcHZDZn5lZWVhWXLlmHChAmYNGlSo2siIroWQ2Iyogff7jqu3peF6gPujWUkIs/weI/QkiVLUF1djeuvvx6PP/44AGcPUW5uLrRaLb766iu0b98ey5cvx8svv4xly5ahT58+jbpXeHg4KisrYTbX361c2/tkNBqvep3Tp0/j2WefRXx8PF566aVG1eIJ3GvMN7gPELnLG8+MnDwGqtwdkE7lAQBKV7+PGkMbTqkPAvyO8a2m7jXm8SC0ZcsW6PV6LF68GC1atAAAbNy4EQAwYMAAtG/fHgAwffp0LFq0CDt27Gj0vWJjY1FZWYmysjK0bdv2svO1Y4Nqxwpdyb/+9S+Ul5eja9eumD9/fp1zBw4cAACsW7cOBQUFuO666/CHP/yh0TUTEQGAoFLDOGImar6aB9idU+rNme8j7JZnOaWeyIc8HoRKSkrQoUMHVwgCnCs0C4KAgQMHutoEQUBCQoJr5ldjJCcnIzc3F3l5eUhNTb3sfF5enutzV1M7hujgwYM4ePBgvZ/Jzc1Fbm4u+vXrxyBERB6hioyBYcj9sGx4DwAgncqDbccq6G+cqnBlRKHD42OELh2vY7VasWvXLgBAv3796pw7d+4cNJrGZ7HaVanXrl172bmKigps27YNWq0WgwYNuup1/v73v+PQoUP1/nffffcBAB577DEcOnQIn376aaPrJSK6lDZpADRdLnxH2XZ9B0dx/X+QEZHneTwIJSYmorCw0DVuJzs7GzabDVFRUXUWJzxy5AiKiorqfaXVUGlpaUhISEB2djaWL1/uardYLHj++edhMpkwbdo0tGrVynXObrcjPz8f+fn5sNvtjb43EZGnGG66B0Jk7TIgMiyZH0C2NH6fRCJqOI8HoRtvvBEmkwnPPfccMjIy8NZbb0EQhDqrPx84cAB/+tOfIMsyhg4d2uh7GQwG/P3vf4fBYMD8+fMxZcoUPP7440hLS0NGRga6deuGp59+us7PnDp1ChMmTMCECRNw6tSpRt+biMhTBJ0RxlF/uGiX+gpYfvqYex0S+YDHg9AjjzyCFi1aYM2aNXjsscdw9OhRhIWFYcaMGQCcg6mnTZuG3NxcxMfH46GHHmrS/fr3748VK1Zg7NixKC4uRlZWFiIiIjBr1ix8+umnaNasmSd+LSIir1LHdID+xgt7JjoKdsH+W4aCFRGFBkH2wp8chYWFWLBgAQ4dOoT27dvjiSeeQEpKCgDnOkNjxozBsGHD8NJLLyEmhlNFa3H6vG9waiu5y1fPjCxLMP+wAGLRfmeDSoOwSX+FulV7r96XPIvfMb7V1OnzXglC11JZWYmoqChf39bvMQj5Br+kyF2+fGYkUxVMX/0VsvksAEDVPA5hU16EoK1/BX3yP/yO8a2mBiGf7jVWiyGIiKh+qrDmMIyYCcA5A1eqOgnL5mXKFkUUxBQJQkREdGWaxO7Q9bnZdezIzYb9/I71RORZDEJERH5I13cSVK2TXMeW7KWQKk8qWBFRcGIQIiLyQ4JKA+PIRwFdmLPBboE5YzFkh03ZwoiCDIMQEZGfUkW0gmHY713H0plCWLf+R8GKiIIPgxARkR/TdrwB2h4XFqS1H8yEPX+bghURBRcGISIiP6fvfwdUrTq4ji0b/w2piuOFiDyBQYiIyM8Jai2Mo2cBWqOzwW6Bef2/OF6IyAMYhIiIAoAqMhaGYRe2JJLOHId162cKVkQUHBiEiIgChLbTjdB2H+U6th/M4vpCRE3EIEREFED0A+6CKqaj69iyaQnE8hMKVkQU2BiEiIgCiGu8kD7c2eCwwbJ+MWS7RdnCiAIUgxARUYBRRcTAOOIR17FUWQzLpiVQYA9tooDHIEREFIA07frU3Y8s72fYD2YqWBFRYGIQIiIKULq+U6COT3YdW7csh1iar2BFRIGHQYiIKEAJKjUMo/4Awdjc2SCJMP+4GJL5rLKFEQUQBiEiogCmCouCIW02IKgBAHJNOSwZ70KWRIUrIwoMDEJERAFOE9cF+gF3uo7F4oOwbf9awYqIAgeDEBFREND2SIOmUz/XsW33atiP7lCwIqLAwCBERBQEBEGAYdhDUEUnuNosWR9CrCxWsCoi/8cgREQUJAStAca0PwJag7PBboFl7duQbSZlCyPyYwxCRERBRBUVD8OIGa5jqeokzBs+gCxLClZF5L8YhIiIgoy2Qyp0qbe5jsXju2Hb+a2CFRH5LwYhIqIgpLvhNqjb9XYd23asguPYLgUrIvJPDEJEREFIEFQwjpwJoXmcq82c+T7ECg6eJroYgxARUZASdGEwjqk7eNq89p+QLdXKFkbkRxiEiIiCmDq6TZ3B0/LZUzBz5WkiFwYhIqIgp+2QCl3fKa5j8cQBWH/+XMGKiPwHgxARUQjQXX9LnZWn7ft/hC3nJwUrIvIPDEJERCHAufL076Fq2d7VZs1eCsfJXAWrIlIegxARUYgQtHoYxz4OwRjpbJBEWNa9A+lsqbKFESmIQYiIKISomrWEIe2PgEoDAJAt52Be8w9uw0Ehi0GIiCjEaOI6wzD0QdexVFkM8/p/cSYZhSQGISKiEKTtMgi6629xHYtF+2Hd+pmCFREpg0GIiChE6fpOhqZjX9ex/UAGbPvXK1gRke8xCBERhShBUMEw4hGoYjq62qxbl8NRwD3JKHQwCBERhTBBo4dx7BMQwls4G2QZ5ox3IZYdU7QuIl9hECIiCnGqsCgYx8+5sCeZwwbzmoWQzp1WtjAiH2AQIiIiqFu0hTHtMUBQAwBkcxXMaxZCttYoXBmRdzEIBhdnJAAAIABJREFUERERAECT2AP6Ife5jqWKEzD/uAiyaFewKiLvYhAiIiIXXcqwutPqiw/CkvURZFlSsCoi72EQIiKiOnR9p0CTNNB17MjfBuvP/1WwIiLvYRAiIqI6ajdoVbfp7mqz71sL294fFKyKyDsYhIiI6DKCWgNj2mN1d6v/+b+w521VsCoiz2MQIiKiegk6I4zj50CIiHG1WbI+gqNwn4JVEXkWgxAREV2RKiwKYeOfgmCIcDZIIsw/vgPx5GFlCyPyEAYhIiK6KlVUHIzj5gAavbPBYYNpzUKI5YXKFkbkAQxCRER0TerYTjCOfQJQaZwNNhPMq9+CdLZU2cKImohBiIiIGkTTphsMox4FBAGAc/Vp0+o3IZkqFa6MqPEYhIiIqMG0HftCP+QB17F8rgzm1W9AMp9VriiiJmAQIiIit+hShkHf/w7XsVRRDPP3/8t9ySggMQgREZHbdL0nQJd6q+tYOlMA0w8LINvMClZF5D4GISIiahTdDZOh7TnWdSyV5sO89p+QHTYFqyJyD4MQERE1iiAI0A+4C9quI1xtYkkOzOveZhiigMEgREREjSYIAvSD74Wm8yBXm1i0H+Yf32EYooDAIERERE0iCCoYhj0ETad+rjaxcB/MPy5iGCK/xyBERERNJqjUMIycCU2nG11tYuFeZxgS7QpWRnR1GqUL8ISjR49i8eLF2LFjB86cOYO4uDiMHz8eM2fORFhYmFvXysrKwrJly7B//36cO3cOUVFRSE1NxcMPP4zevXt76TcgIgp8tWHIIstwHN0O4HwYWvcOjGmPQdDoFK6Q6HIB3yO0d+9eTJkyBenp6WjVqhWGDx8Ok8mE9957D3fddReqq6sbfK0FCxZg5syZyM7ORps2bTB8+HBERkZi3bp1+N3vfoeVK1d68TchIgp8gkoDw6hHoenY19XmDENv/397dx4eVXXwcfx7Z0kmCVtYQwgYICQssojK4gYKuFeroghU7Su+akWtVu1ji4K2iO1ra60WWxeeqqisWiqgUhWxoDWIbIpsAYEQyEJIAslkMtt9/wgZkmYiBJKZzMzv8w96zr13zuU53PnNueeei+mtCmPLRIIzTNM0w92IU+X1ern88svJzc3lqaeeYvz48QC4XC4efPBBVq5cyeTJk5k+ffoJj7Vu3TomT55MQkICL730EsOHDw/UzZ8/nxkzZhAXF8eKFStITU1tlvPxeHyUljqb5dhyXKdO1W/RLio6GuaWSKRQn2k80+/F9fFf8e75OlBm7dqXhMsfwLA7wtiy5qf+Elrt2iVit1tPef+IHhFavnw5ubm5jBw5MhCCABwOB7NmzSIxMZGFCxdSVlZ2wmMtXrwYgDvuuKNOCAK4+eabGTVqFG63mxUrVjTtSYiIRCHDYsMx9mfYeh+/nvoObsP5/h8w3frBJy1HRAehlStXAjBu3Lh6dcnJyQwfPhyPx8Pq1atPeCyHw0FmZiYjRowIWt+rVy8ACgoKTqPFIiKxw7DYcFx8F7bM44/W+wtycC5/BtN18tMWRJpTRAehHTt2AJCVlRW0PiMjA4Bt27ad8FhPPPEES5cu5Zxzzglav2nTJgBSUlJOpakiIjHJsFhwjJqCve+oQJm/6HucS5/GX1ESxpaJVIvoIFRYWAhAly5dgtZ37ty5znanauXKlaxfvx673R509ElERBpmGBbiL/wp9gFjA2X+kjyc783Cf+T0rs8ipyuiH593OqvvMzscwSfe1ZTXbHcqtm/fzq9+9Sugev5Qt27dTvlYJ2K3WwOT7KT56e9aGkt95vSY195NSbt2lH5ePSfTPFqEa9nTdJ04nbjOPcLcuqan/hIZInpEyGqtniVuGMYPbneqD8Zt3ryZ2267jdLSUi6++GLuv//+UzqOiIhUX6vbj55I+7G3Bcp85SUcmPs4rv3bw9gyiWURPSKUlJREaWkplZWVQetdLhcACQkJjT72hx9+yKOPPkplZSVjx47lT3/6ExZL8+ZGPT4fGnq0VRpLfaaJ9boYx0VWXKv/DqaJ31XOgTefIGHMz7ClnxXu1p029ZfQiunH52vmABUVFQWtr5kbVLPdyZo9ezYPPPAAlZWVTJo0ieeff564OK2IKiLSVOx9L8Ix5h6wHPs97nNT+dHzuLeuCmu7JPZEdBCqeVosJycnaH1NeUNPlf03v9/Po48+yvPPP4/FYmHatGnMmDEjcAtORESajr3XuSRc+RDYj43amyZVq1+j6uslpzylQaSxIjoIjRpV/ThmsEUOS0pKyM7Oxm63c/7559erD+axxx7jH//4B4mJifz1r3/l1ltvbdL2iohIXbbUfiRe82uMxHaBMvfXS6j6998x/d4wtkxiRUQHoXHjxpGamsqaNWt46623AuUul4tp06bhdDoZP348HTt2DNR5PB527drFrl278HiOvxF5yZIlvPPOO9hsNl588cVAyBIRkeZl7dCdxGsfw9Kua6DMs/3fVH7wJ61CLc0uot81BpCdnc2dd96Jy+ViwIABpKWlsWHDBgoLC+nfvz9z586lVatWge3379/PmDFjAPjkk09IS0vD5/MxZswYDh48SJcuXRg2bFiDn3fBBRfw4x//uFnORZOlQ0MTGaWx1GdCw3SVU7niz/gKdgbKLMmpJFz+IJbWncLYssZRfwmt050sHdFPjQEMHz6cRYsW8Ze//IW1a9eSk5NDWloa48ePZ8qUKXVCUEO2b9/OwYMHgepXaCxdurTBbdu0adNsQUhEJJYZjlYkXPUIrs/m4N2VDYC/5ADOJb8l4bIHsHbuFeYWSjSK+BGhaKIRodDQrzVpLPWZ0DJNP+51/8C9odaPUqu9+lUdGcHfB9mSqL+EVkw/Pi8iItHHMCzEn3sDjlFTwDj2Befz4Fr5N6rWLsY0/eFtoEQVBSEREWmR7FkXknDVwxCfFChzb1xG5YrnMd3BF9IVaSwFIRERabFsqf1Ium4GluTj73n07duI858z8Zflh7FlEi0UhEREpEWztOlM4rWPYe0xJFDmL8mj4h9P4t27MYwtk2igICQiIi2eEZdAwqX3EzfkquOF7koqVzxH1bp3Mf2aNySnRkFIREQigmGxED/sRhxj7wFbfKDcvf49Klf8CdNVHsbWSaRSEBIRkYhi7zWMxOumY7RNCZT5cr+h4t0Z+AqCv3tSpCEKQiIiEnGsyd1Ium46tjPOCpSZ5cU433sa9+YP9dJWOWkKQiIiEpGMuEQcl95H3LAbwTj2dWb6qPpyPq5/PY9ZVRHeBkpEUBASEZGIZRgW4odcRcKPfoWRlBwo9+7dQMXix/Ee3B7G1kkkUBASEZGIZ0vpQ+INv8HafWCgzKw4TOWy31H11TuYfm8YWyctmYKQiIhEBYujNQmXP3jsVtmxV3OYJu4NS3G+Nwv/kcLwNlBaJAUhERGJGjW3yhKvnYbRpkug3F+4m4p3puPeukoTqaUOBSEREYk61s69SLrhSWyZFx4v9LioWv0alR/+CX9FSfgaJy2KgpCIiEQlw+4gYfQUHGPuqfPiVl/uZioWP4Yn50uNDomCkIiIRDd772EkjZ+Jtfug44VVFbhW/g3XRy/gd5aGr3ESdgpCIiIS9SxJySRc/iDxF/4U7I5AuXfPeioW/hrP9tUaHYpRCkIiIhITDMMgrt9okm74LdbUfscr3E5cn82h8v0/4D9SFL4GSlgoCImISEyxtOlEwlW/rDc65MvbQsWiaVRtWIbp07pDsUJBSEREYk5gdOjGWVh7DD5e4XPj/moxznena1XqGKEgJCIiMcvSqj0Jlz2A45K7MRLaBMr9JQeoXPo0late0WTqKKcgJCIiMc0wDOwZI0i66Wns/S8BjECdd8fnVCz4VfUb7fWajqikICQiIgIY8Uk4LriVxB8/hqVD9+MVnkqqvpyPc/F0vPu/DV8DpVkoCImIiNRi7dybxOueIP68n0BcYqDcX3qAyvf/gPODZ/GV5IWxhdKUbOFugIiISEtjWKzEnTkWW8Zw3GvfwbPtM6B6nSFf7mac+7/F3ncUcedch6XW3CKJPBoREhERaYDF0RrHRT8l8foZWFMyj1eYfjxbP6Vi/i+pWv9PTI8rfI2U06IgJCIicgLWjukk/OhXOMbdV+et9nhcuNf9g4p5j+D+9iNMnyd8jZRTYphaU7zF8Hh8lJY6w92MqNepU2sAioqOhrklEinUZ6Q20+fF890nVK1/D6oq6tQZrTrQYdQEWg8cxaHDlWFqYWxp1y4Ru916yvsrCLUgCkKhoS81aSz1GQnGrKrAvekD3N/+C7zuOnW2dl2wDb4aW5/zMCyn/iUtJ6YgFEUUhEJDX2rSWOoz8kP8zlLc65fi2boKTF+dOqNNZ+LP+hG2PiMxLHo+qTkoCEURBaHQ0JeaNJb6jJwM/5FC3BuW4tnxOZj+OnVGqw7EDboce9+LMGzxYWphdFIQiiIKQqGhLzVpLPUZaYx2tgpK1iym/JvP6gciR2vsAy8lrt/FGI5WYWphdFEQiiIKQqGhLzVpLPUZaYya/lKQk4N70/LqESJ/3Vtm2OKwZ11E3MBLsbTpHIZWRg8FoSiiIBQa+lKTxlKfkcb47/7iLz+Me/OH1XOIfHUnVWMY2NLPxn7mOKwpmRiGgTSOglAUURAKDX2pSWOpz0hjNNRf/JVH8Gz5GM+WlZhV5fX2s3ToQdyAsdgyRmDY4kLS1migIBRFFIRCQ19q0ljqM9IYJ+ovprcKz47PcX+zArOsoF69Ed8KW9YFxPUbjaVtSrO2NRooCEURBaHQ0JeaNJb6jDTGyfYX0/Tj27sJ95aP8OV9F3Qba2o/7P0uxpY+FMOqx++DOd0gpL9VERGRMDAMC7b0s7Cln4XvcF71bbOdn9dZnNF3YCu+A1sxHK2xZYzE3vdCrO27h7HV0UcjQi2IRoRCQ7/upbHUZ6QxTqe/mFUVeHZ+gWfrKvwleUG3sXRMx555AbaM4VgcrU+rrdFAt8aiiIJQaOhLTRpLfUYaoyn6i2ma+Apy8Gz9FO/uryDYy1wNK9buA7H3OQ/bGUNidoK1glAUURAKDX2pSWOUVbj5OucQ3+4q5mh5FY44K2ektAYM9uYfweX24YizktUjmQsHp9I2qf6XUVmFm39vOsCOfSW43D5sVgOr1YLL7aP0aBVVHh/xdisd2yVwZs/2XDg4FaDOPif6jBOdQ1MdS06sqa8xZlUFnl3ZeLavxl/0ffCN7A5sZ5yFPWM41m5nxtR8IgWhKKIgFBoKQnIy3B4fb3+8k8+/OYjPf3KXScOANolxWAxwe/3YLAYuj58qj+/EOzdCetfWTP3xmXRom1CvrnbocVZ5KT1aRWmFm2BXeqvF4IJBXZk0tg92m14M2lSa8xrjO7wfz441eHO+xHSWBt8oLrF6baJe52Lt1g/Dam/ydrQkCkJRREEoNBSE5ETcHh9/WriJ7bkNfNG0EO1axTFqSCqjz0ojIc7a6OBWI6t7Ox68aTBxp/FlIseF4hpj+v34DmzFk/MF3u+/Bo8r+Ib2BGxnDMaWfja27oMw7NH3njMFoSiiIBQaCkJyIq99sI1/bzoQ7macNIsBrRPjKKtwn3jjBowaksptl/dtwlbFrlBfY0xvFd59m/Huysa7b1Pw+UQAVhvW1P7YzhiCrccQLK3ah6R9zU1BKIooCIWGgpD8kLLyKh5+8YtGj6pEOqvF4A9Tz9ecoSYQzmuM6XHh3bsR7+6v8OZ+U/+VHrVYOvTA1n0Q1u4DsXbJwLBE5oig1hESEWlC/97c+FtL0cDnN1m96QBXn5ce7qbIaTDsDuwZI7BnjKgeKcr9Fu/36/Du2wjuyjrb+ov34S7eBxuXQVwCtm4DsKadia3bACxtOoXpDEJPQUhEpJYd+0rC3YSw2b6vREEoihi2eOw9z8be82xMvxdf/k68ezbg3bcR80hh3Y3dldWB6ft1VAFGm87YuvXH2q0/1q59sSS0Ccs5hIKCkIhILS530z7hFUli+dyjnWGxYUvthy21H+bIifhLD+LL3Yw39xt8B7eD31tne/NIIZ4jhXi2rgLAktwNa2pfrF37Yk3JxJLYNgxn0TwUhEREanHEReY8iaYQy+ceSwzDwJqcijU5lbhBl2N6XPgObMW7/1t8ed/hLz1Ybx9/SR7+kjw8Wz6pPkbbLli7ZGLrmomlS28sbVMwDEuoT6VJKAiJiNSS2SOZLXti8/ZYVo/kcDdBwsA4thij7YyzAPCXF+PL+w5v3nf4Dm7DrKj/78EsK8BbVoB3x+rqgvgkrJ17Y+3Su/rPTj0x4pNCeRqnTEFIRKSWIb078I9/7w53M0LOajECK1pLbLO06oAl60LsWRdimibmkQK8B7bhO7ANX/4OzIrD9XeqqsCXuxlf7uZAkdG2C9ZOPbF27ImlUzrWDj0w4uovAhpuCkIiIrVs3FUc7iaExXlnpujReanHMAyMtinEtU2BfqOrg1F5Mb78HfgO7sBXkHPs5bD1n7QMjBrlfHn8eG1TsPc8m7hzrm8xj+srCImI1BLLT42JnIhhGBitO2Jp3RF7n/MAMN1OfIW78RXk4Cvcjb9wN2ZVedD9zbJ83BuXY+ncG3v60FA2vUEKQiIitcTqk1NffJvP9aN6a1RIGs2IS8SWdia2tDMBqkeNjhZVh6Oi7/Ef2ovv0F7wHFvHyGLF0qpDGFtcl4KQiEgtsfrklBZUlKZiGAZGm85Y2nTGnjECANP0Yx4pwleSh6VNZ6zt08LcyuMi81k3EZFmkhnDT05t3RNkEqxIEzAMC5a2XbCnD21RIQgUhERE6rhoUFcsRrhbER7f5x/F7YnNW4MSuxSERERqadsqnvgYvT3mcvuY98nOcDdDJKSiYo7Q999/z+zZs/n6668pLi4mJSWFK664grvuuovExMRGHaugoIAXX3yRL774gvz8fDp27Mgll1zC1KlTad++fTOdgYi0FLkFR6msit1RkTWbD/LjC3tp0rTEjIgfEdq8eTPXX389S5cupWPHjowePRqn08nf/vY3br75ZsrLgz/CF0xubi433HAD8+fPx+FwcPHFF2O1WnnzzTe57rrryM/Pb8YzEZGWYMGnOeFuQljVTJoWiRURHYS8Xi+/+MUvcDqdPPXUUyxatIjnn3+ejz/+mEsuuYTt27fz7LPPnvTxHn30UYqKipg6dSpLly7l+eefZ8WKFdx8883k5+czY8aMZjwbEWkJ9uYfDXcTwm671lKSGBLRQWj58uXk5uYycuRIxo8fHyh3OBzMmjWLxMREFi5cSFlZ2QmPtW7dOtatW0d6ejr33ntvoNxqtfLYY4+RmprKqlWryMmJ7V+LItHO66u/Qm6sidW1lCQ2RXQQWrlyJQDjxo2rV5ecnMzw4cPxeDysXr36pI81ZswYLJa6fy12u51LLrkEgE8++eR0my0iLZjNGqOPjNUSq2spSWyK6CC0Y8cOALKysoLWZ2RkALBt27aTPlZmZuZpH0tEItcZKa3D3YSw01voJZZEdBAqLCwEoEuXLkHrO3fuXGe7UB1LRCLXhIszwt2EsNJb6CXWRPTj806nE6ieExRMTXnNdidzrISEhNM+1qmy26106qRfo6Giv2sJplOn1nTtkMjB4ub7t96SjR3Wg4z0lvMeqEima0xkiOgRIau1+j62YfzwPX3TPPHkx6Y8lohEtpGDYnNE5MzeHbjzxwPD3QyRkIroEaGkpCRKS0uprKwMWu9yuYCGR3n++1hAkxzrVHk8PkpLY/NXaCjV/EorKtJj0hLcx9n7wt2EkLJaDC4Y1JVJY/tQpmvQadM1JrTatUvEbj/1Cf4RHYQ6d+5MaWkpRUVFdO/evV59zXyemvk9JzrWli1bKCoqClrfmGOJSOQqK6/iiNMd7maERHLreC4a3JXRZ6VpJWmJWREdhLKystixYwc5OTkMHTq0Xn3Nmj8NPVX238f69NNPG1wnqDHHEpHI9e/NB8PdhCZlAFeMOIO9+UdwuX044qxk9UjmwsGpCj8iRPgcoVGjRgGwYsWKenUlJSVkZ2djt9s5//zzT/pYH330EX6/v06dx+MJrB908cUXn26zRaQF2xFlqyqbwJ78I9zxowFMu/UcHrr5LK4+L10hSOSYiA5C48aNIzU1lTVr1vDWW28Fyl0uF9OmTcPpdDJ+/Hg6duwYqPN4POzatYtdu3bh8XgC5UOHDmXgwIHs2rWLZ599NjAp2ufz8dRTT3Hw4EEuuugi+vXrF7oTFJGQi8ZVlb/bU8JDf1nD6x9uw+ONvvMTOR2GGeGPQWVnZ3PnnXficrkYMGAAaWlpbNiwgcLCQvr378/cuXNp1apVYPv9+/czZswYoHqV6LS0tEDdzp07+clPfkJpaSm9evWiT58+bN26lX379tGtWzfmzZvX4DpDTUGTpUNDExnlh/xx/ga27ImuUaHa+qS15aEJQ4g7jcml8sN0jQmt050sHdEjQgDDhw9n0aJFXHbZZRw4cIBVq1bRunVr7rnnnnoh6ET69OnDu+++y/XXX8/Ro0f59NNPAbjllltYuHBhs4YgEWkZon1l6Z37y3j6rfWUVcTGhHCRE4n4EaFoohGh0NCvNfkhi1bl8MGX0f/4fO1H5u02jQ41JV1jQivmR4RERJrSvvzY+PLy+U0+23iAZxdswu3RvCGJXQpCIiK1RONk6R+yPbeUeZ/sDHczRMJGQUhEpBabNfYui2s2H9ScIYlZsfcvXkTkB+Qdqgh3E0LO5zdZvelAuJshEhYKQiIix+QWHKW80nPiDaPQ9ihbSFLkZCkIiYgcs+DT4K/YiQWxNjdKpIaCkIjIMXtj5ImxYBxxeoReYpOCkIjIMV5f7C6rltUjOdxNEAkLBSERkWNsViPcTQgLq8XgwsGp4W6GSFgoCImIHBPtr9doyAWDuupt9BKzFIRERI6ZcHFGuJsQclnd2zFpbJ9wN0MkbBSERESO6d6lNZ3aJYS7GSGTntKaX0wYrHeNSUxTEBIRqeXxW8/Gaon+uUIWA35+o0KQiIKQiEgtrRLj+P3dI4izRffl8cLBqZoXJIKCkIhIPe3bJPD8zy/knKxO4W5Ks9C8IJHjbOFugIhISxRnt3LPdQMpq3CzPqeYTTuL2HOgjCPO5n8FR7tWcYwaksros9IAWL3pAJtyijhwyEmV148BxNutpHVOonvn1uwvPEpRqQuX24vPb1Ll8Qc9rtVicMGgrkwa20e3xESOMUzTjN0VxFoYj8dHaakz3M2Iep06VT8iXVQUu6sIS+PU7jNlFW5WbzrA9n0luNw+HHFWsnokM6RPRzbuPMT2fSWUV3qorPLi9Zo43V6qPD5+6EprMaBdq3hGDEhh3LndT/uWVUNt1O2w0NA1JrTatUvEbj/1YK8g1IIoCIWGLlLSWOoz0hjqL6F1ukFIc4REREQkZikIiYiISMxSEBIREZGYpSAkIiIiMUtBSERERGKWgpCIiIjELAUhERERiVkKQiIiIhKzFIREREQkZikIiYiISMxSEBIREZGYpXeNtSCmaeL1Bn9rtDSdmnfSeDy+MLdEIoX6jDSG+kto2WwWDMM45f0VhERERCRm6daYiIiIxCwFIREREYlZCkIiIiISsxSEREREJGYpCImIiEjMUhASERGRmKUgJCIiIjFLQUhERERiloKQiIiIxCwFIREREYlZCkIiIiISsxSEREREJGYpCImIiEjMUhASERGRmKUgJCIiIjFLQUhERERiloKQiIiIxCwFIREREYlZCkIiIiISs2zhboBIU9mxYwevvvoqa9eu5dChQzgcDvr168eECRO4+uqrg+7z/fffM3v2bL7++muKi4tJSUnhiiuu4K677iIxMTHoPl988QWvvPIK27Ztw+Vy0atXL26++WbGjx+PYRjNeYoSQqfSNyS6/POf/2Tx4sVs27aNyspKOnTowIgRI7jzzjvp3bt3ve3ff/993njjDXbv3o3P56Nv377cdtttXHrppUGP73K5eOONN1i6dCm5ubkkJCRw7rnncs8999C3b9/mPj05xjBN0wx3I0RO18qVK7n//vvxeDycccYZZGRkUFxczObNm/H7/dx4443MnDmzzj6bN2/mtttuw+l0MmjQILp27cr69espKioiKyuLt99+m1atWtXZZ968eTzxxBPY7XaGDx+O3W7nyy+/pLKykuuvv56nn346lKctzeRU+oZED9M0efjhh1m2bBk2m42BAwfSvn17tm3bRl5eHg6Hg9mzZ3PBBRcE9nnmmWd49dVXSUxMZPjw4bjdbtauXYvH4+Hee+/lvvvuq/MZVVVV3HHHHaxdu5ZOnToxdOhQDh48yObNm7Hb7bz00kucf/75oT712GSKRLgjR46Yw4YNMzMzM81XX33V9Pv9gbqNGzeaZ599tpmZmWkuX748UO7xeMwxY8aYmZmZ5qJFiwLllZWV5t13321mZmaaTz75ZJ3P2b17t9mvXz9z6NCh5pYtWwLleXl55tixY83MzEzzgw8+aMYzlVA4lb4h0WXJkiVmZmamef7555tbt24NlHu9XvPZZ581MzMzzZEjR5rl5eWmaZrmF198YWZmZpqjRo0y9+/fH9h+69at5vDhw82srCxz06ZNdT7jueeeMzMzM83bb7/drKysrPPZWVlZ5nnnnRc4vjQvzRGSiPfRRx9RWlrKsGHDmDJlSp3bU4MHD+buu+8G4L333guUL1++nNzcXEaOHMn48eMD5Q6Hg1mzZpGYmMjChQspKysL1L366qv4fD6mTJlC//79A+WpqalMnz49sI1EtlPpGxJdFi9eDMBDDz1U5xaV1WrlgQceoE+fPhQXF/P5558D8NJLLwHw4IMP0q1bt8D2ffv25YEHHsA0TebMmRModzqdvPHGG1gsFn7zm9/gcDgCdddeey1XXnklhw4dYsmSJc16nlJNQUginsfjYcCAAYwePTpofa9evQAoLCwMlK1cuRKAcePG1dvS/r9XAAAM+klEQVQ+OTmZ4cOH4/F4WL16daD8008/BQh6v/+8886jdevWfPPNNxQUFJzyuUj4nUrfkOjSpk0bevfuzTnnnFOvzjAMevbsCUBBQQHl5eWsXbsWq9XKmDFj6m1/6aWXYhgGq1atwufzAfDVV19RXl7OgAED6gSnGpdddhlwvC9K81IQkog3YcIE3n33XaZMmRK0ftOmTQCkpKQEynbs2AFAVlZW0H0yMjIA2LZtGwCHDh2iuLgYu90eCFa1Wa3WQPn27dtP8UykJWhs35DoM3v2bN5//326d+9er87n87FlyxYAunbtyq5du/D5fKSmpgadN9a+fXs6dOiAy+Viz549wPFrRGZmZtDPVx8LLQUhiWoFBQXMnTsXgCuvvDJQXjM61KVLl6D7de7cuc52NX927NgRiyX4P5v/3kciU2P7hsSWt99+m7y8PNq1a8fIkSNP2F+g4etJQ/vUlB86dAi/399kbZfg9Pi8tDh33XUXq1atOqltP/nkE9LS0oLWHT16lHvuuYeKigrOPfdcrrrqqkCd0+kEqHNvvraa8prtTrQ9QHx8fJ1tJTI1tm9I7PjPf/7D//3f/wHw8MMPk5SUREVFBdC4a0PNnwkJCT+4fc22ekKxeSkISYvToUOHoPfNg7HZgnfh4uJi/vd//5ctW7aQlpbGc889V2cStdVqxe/3n3DdH/PY6hI1o0Ans06QqRUpIlpj+4bEhk8//ZQHHngAt9vNxIkTufHGG4Hq/gKNuzbU7NOQ2sdSP2t+CkLS4syaNeu09t+xYwc/+9nP2L9/Pz179uTvf/87HTt2rLNNUlISpaWlVFZWBj2Gy+UCjv9iS0pKqlMeTFVVFYAW24twje0bEv3mzp3L008/jc/nY/LkyTz++OOBulO5Npxon9rlPzTSJE1Dc4Qkqnz22WfcfPPN7N+/n6FDhzJv3jy6du1ab7uae/ZFRUVBj1NzD79mu9r37Bv6hfbf+0hkamzfkOjl9XqZPn06M2fOxO/38+CDDzJ9+vQ6IzY114aG+gs0fD1paJ+aJ0/bt2+P3W4//RORH6QgJFFjwYIF/OxnP6OiooJrrrmG119/neTk5KDb1jwRlJOTE7S+prxmu3bt2tGlSxfcbjd79+6tt73P52P37t119pHI1Ni+IdHJ5XJx1113sWDBAhISEnjuuecCa5LVlpGRgdVqJS8vL+go4uHDhykuLiY+Pp4zzjgDON53du7cGfSz1cdCS0FIosKSJUuYMWMGPp+P++67j2eeeYa4uLgGtx81ahQAK1asqFdXUlJCdnY2dru9zhL3Nfv861//qrfP559/ztGjR+nbt2+dx/Ql8pxK35Do4vP5mDp1KmvWrKFDhw688cYbXH755UG3jY+PZ8SIEXg8nqDr/qxYsQLTNLngggsCoztnn302rVq14ptvviE/P7/ePh9++CFAg2ujSdNSEJKIt3v3bmbMmIFpmtx3333ce++9J9xn3LhxpKamsmbNGt56661AucvlYtq0aTidTsaPH19nbtHkyZOxWq289NJLbNy4MVB+4MABfvvb3wLVT7xJZDuVviHR5a9//Str1qwhMTGR119/nUGDBv3g9rfeeisAv//97wNrBUH1OkB//vOfgbrXhvj4eG666Sa8Xi+PPvoo5eXlgbr33nuPDz/8kOTkZG666aYmPCtpiF66KhHvoYceYtmyZdjtdi677LIGn97o2rUrDz30UOD/s7OzufPOO3G5XAwYMIC0tDQ2bNhAYWEh/fv3Z+7cufUeW3355Zf54x//iNVqZdiwYTgcDrKzs3E6nUFf7CqR6VT6hkSHsrIyRo8ejdPpJD09nYEDBza47Y9+9KPACOKMGTOYP39+YITI5/ORnZ2Nx+Ph5z//Offcc0+dfZ1OJ7fccgvffvst7du359xzzyU/P59NmzYRFxfHyy+/zMiRI5v1XKWagpBEvLPPPrvOL6qG9OnTh2XLltUp27FjB3/5y19Yu3YtTqeTtLQ0LrvsMqZMmdLgF90nn3zCa6+9xpYtWwLL7U+cOJHrrruuwcUWJfKcSt+QyPfRRx+d1KgywC9/+cvAivamafLuu+8yb948cnJyiI+PJyMjg//5n/9h7NixQfd3Op288sorvP/++xw4cIDk5GQGDx7M1KlT67zjTJqXgpCIiIjELP18FRERkZilICQiIiIxS0FIREREYpaCkIiIiMQsBSERERGJWQpCIiIiErMUhERERCRmKQiJiIhIzFIQEhERkZilICQiIiIxS0FIREREYpaCkIiIiMQsBSERERGJWQpCIiIiErMUhERERCRmKQiJiIhIzLKFuwEiIs1t7dq1LFq0iA0bNnDo0CG8Xi/JyckMGTKESZMmMXLkyHr7fPHFF7z22mt89913HD16lPT0dG666SYmTpxIv379ANi+fXu9/b766ivmzp3L+vXrKS0tpU2bNgwZMoRbbrkl6OeISHgZpmma4W6EiEhz+eMf/8jLL78MQPv27enatSvl5eXk5eXh9XoB+M1vfsOECRMC+7z44ov8+c9/BqBjx46kpKSwZ88eysvLufTSS/nXv/4F1A9Cf/jDH3jllVcAaNu2LWlpaRQWFlJUVATAHXfcwSOPPNK8JywijaIgJCJRKzs7m1tvvRWLxcLMmTO57rrrsFiqZwTk5+fzyCOPsHbtWjp27Mjq1auxWCx8/vnn3H777VgsFn79618zefJkLBYLLpeLZ555hjfffDNw/NpBaP78+cyYMYM2bdrw+OOPc8011wBgmiYffPAB06ZNw+l0MnPmTG688cbQ/kWISIM0R0hEotbq1auJi4tj3Lhx3HDDDYEQBJCSksLPf/5zAA4dOkRxcTEAzz33HAA//elPueWWWwL7OBwOHn/8cUaNGlXvc9xuNy+88AIAs2bNCoQgAMMwuPLKKwMjQS+88EJgJEpEwk9BSESi1sMPP8zmzZt55plngtY7HI7Af7tcLgoKCvjmm28AmDRpUtB9br311nplNXOPkpKSGDNmTND9rrnmGiwWCwUFBXz33XeNPRURaSaaLC0iUc0wDCwWC+vWrSMnJ4fc3Fz27dvH9u3b2bt3b2A7v9/Pzp07MU2TxMREunfvHvR4Z555Zr2ynTt3AuDxeJg8eXKDbbFarfj9fnbv3s2gQYNO88xEpCkoCIlI1DJNk9dff505c+ZQWFgYKDcMg549e3Lttdfyz3/+M1BeUlICQFJSUoPHbNWqVb2yo0ePAtW3yNavX3/Cdh05cuSkz0FEmpeCkIhErdmzZwfm7lx55ZVcdNFFZGRk0KtXL5KSktizZ0+dIJSQkABAeXl5g8esqKioV1az34ABA3j33Xeb8hREpJkpCIlIVPJ4PMyZMweAqVOncv/999fbJj8/v87/Z2VlAVBZWcm+ffvo0aNHvX22bdtWr6xnz54A7NmzB6/Xi81W/9JqmibZ2dmkpKSQmppKXFxc409KRJqcJkuLSFQqKSnB6XQC1SM1wSxatCjw316vl+7du9O3b18AFi9eHHSfBQsW1Cs799xzad26NRUVFQ2OCC1dupTbbruNK664ol4AE5HwURASkajUvn172rVrB8Brr71GWVlZoO7w4cM88cQTLFu2LFDmcrkAuO+++wCYM2cOCxcupGapNY/HwwsvvMDy5cvrfVZiYiJ33nknAE899RTvvPMOfr8/UP/xxx8zY8YMAK644oqgI00iEh5aUFFEotbbb7/Nk08+CVTP40lPT8ftdrN37168Xi/9+/fn4MGDlJSU8OKLLwYefX/mmWd49dVXAejUqRNdu3Zl7969lJWVMXjwYDZt2oTVaq3zGLxpmkyfPp2FCxcCkJycTFpaGgUFBYGJ2kOHDmXOnDkkJiaG8q9BRH6AgpCIRLX//Oc/vPLKK+zcuZPDhw/TqlUrevfuzVVXXcVNN93EY489xpIlS7jxxhuZOXNmYL+PP/6YN998ky1btuByucjIyGDixIn07t2bSZMmkZSUFPQJsTVr1jB//nw2btxISUkJ8fHx9OnTh6uvvpoJEyZobpBIC6MgJCLSCKtWreKuu+4iPT2dFStWhLs5InKaNEdIRKSWmpGbLVu2BK3/7LPPAOjfv38omyUizURBSESklvT0dDZu3Mjvfve7Ooswer1eFixYwIIFCzAMg4kTJ4axlSLSVHRrTESklu+//55JkyZx+PBh7HY7PXr0wOFwkJeXR2lpKRaLhUceeYTbb7893E0VkSagICQi8l9KSkqYN28eH3/8MXl5eVRWVtKpUyfOOeccJk2axODBg8PdRBFpIgpCIiIiErM0R0hERERiloKQiIiIxCwFIREREYlZCkIiIiISsxSEREREJGYpCImIiEjMUhASERGRmKUgJCIiIjFLQUhERERiloKQiIiIxCwFIREREYlZCkIiIiISsxSEREREJGYpCImIiEjM+n92mC1zRlHWXQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(\n",
    "    p1a_df[\"age\"], p1a_df[\"survived\"] +\n",
    "    np.random.uniform(-0.05, 0.05, len(p1a_df[\"survived\"])), 'o')\n",
    "plt.xlabel(\"age\")\n",
    "plt.ylabel(\"survived\")\n",
    "xdata = np.linspace(-300, 300, 100).reshape(-1, 1)\n",
    "plt.plot(xdata, p1a_model.predict_proba(xdata)[:, 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### P1B"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this part, we'll build another 1D model called `p1b_model` that tries to predict whether or not someone survived based on how much they paid for their ticket.\n",
    "\n",
    "You'll need to start by creating a new dataframe called `p1b_df` that has \"survived\" and \"fare\" data. \n",
    "\n",
    "Once you've created your model, plot the model's predictions vs. the data just like you did at the end of p1a. \n",
    "\n",
    "You should see that the chance of survival for someone who paid a very low price was around 30%, but those who paid \\$200 had a 90% chance of survival."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "p1b_df = df[[\"survived\", 'fare']].copy()\n",
    "p1b_df = p1b_df.dropna()\n",
    "p1b_df = p1b_df.sort_values('fare')  # 排序后才可画出连续的线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>633</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>0</td>\n",
       "      <td>263.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>1</td>\n",
       "      <td>263.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>737</th>\n",
       "      <td>1</td>\n",
       "      <td>512.3292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>1</td>\n",
       "      <td>512.3292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679</th>\n",
       "      <td>1</td>\n",
       "      <td>512.3292</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived      fare\n",
       "271         1    0.0000\n",
       "597         0    0.0000\n",
       "302         0    0.0000\n",
       "633         0    0.0000\n",
       "277         0    0.0000\n",
       "..        ...       ...\n",
       "438         0  263.0000\n",
       "341         1  263.0000\n",
       "737         1  512.3292\n",
       "258         1  512.3292\n",
       "679         1  512.3292\n",
       "\n",
       "[891 rows x 2 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p1b_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "512.3292"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p1b_df['fare'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p1b_model = LogisticRegression()\n",
    "p1b_model.fit(p1b_df[['fare']], p1b_df['survived'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'survived')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(\n",
    "    p1b_df[['fare']],\n",
    "    p1b_df['survived'] + np.random.uniform(-0.1, 0.1, len(p1b_df['survived'])),\n",
    "    'o')\n",
    "plt.plot(p1b_df[[\"fare\"]], p1b_model.predict_proba(p1b_df[[\"fare\"]])[:, 1])\n",
    "plt.xlabel('fare')\n",
    "plt.ylabel('survived')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### P2: Two Dimensional Logistic Regression "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this part, we'll build a model that gives a survival prediction probability based on both age AND fare."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "p2a_df = df[[\"survived\", \"fare\", \"age\"]].copy()\n",
    "p2a_df = p2a_df.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### P2A: Training the Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this problem (2a) there is no code for you to write, but you should still run and understand the following pieces of code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p2a_model = LogisticRegression()\n",
    "p2a_model.fit(p2a_df[[\"fare\", \"age\"]], p2a_df[\"survived\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below, we can visualize the predictions on a contour plot, with fare on the x-axis and age on the y-axis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "xx, yy = np.meshgrid(np.linspace(0, 550, 20), np.linspace(0, 80, 20))\n",
    "\n",
    "p_survive = p2a_model.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]\n",
    "p_survive = p_survive.reshape(xx.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x18eee588a08>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.contourf(xx, yy, p_survive)\n",
    "plt.xlabel(\"fare\")\n",
    "plt.ylabel(\"age\")\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you have plotly installed, the code below makes a nicer looking plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "contour = go.Contour(x=xx[0, :], y=yy[:, 0], z=p_survive, colorscale='Viridis')\n",
    "contour_fig = go.Figure(data=[contour])\n",
    "\n",
    "contour_fig.update_layout(xaxis_title='fare', yaxis_title='age')\n",
    "\n",
    "plotly.offline.iplot(contour_fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### P2B: Using the Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below, compute the probability of someone surviving if they paid \\$50 for a ticket and were 35 years old according to your model. *Hint:* Use the `predict_proba` function of `p2a_model`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.54224409, 0.45775591]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_survive_50_dollars_35_years_old = p2a_model.predict_proba([[50, 35]])\n",
    "p_survive_50_dollars_35_years_old"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Optional: Use the coefficient and intercept below to compute the same probability using the algebraic equation that defines logistic regression. Some key values are given below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.01725818, -0.01757775]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p2a_model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.41706811])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p2a_model.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.45770134187392447"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_survive_50_dollars_35_years_old_computed_manually = 1 / (\n",
    "    1 + np.exp(0.4031 - 0.0172 * 50 + 0.0179 * 35))\n",
    "p_survive_50_dollars_35_years_old_computed_manually"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### P2C: Visualizing the Decision Regions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below, create a plot showing the decision regions of your model.\n",
    "\n",
    "Hint: The code will be very similar to the code from P2A, except that it should be using `predict` instead of `predict_proba`.\n",
    "\n",
    "Note: To make your decision boundaries look better, when you call plt.contourf, assign `cmap = sns_cmap`. See the hw5 lecture for example code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'survived')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "# setting the cmap of your plot to sns_cmap  will make the decision boundary plot look nicer\n",
    "# note: This doesn't work if you use plotly\n",
    "sns_cmap = ListedColormap(np.array(sns.color_palette())[0:2, :])\n",
    "\n",
    "xx, yy = np.meshgrid(np.linspace(0, 550, 20), np.linspace(0, 80, 20))\n",
    "survival_predictions = p2a_model.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "survival_predictions = survival_predictions.reshape(xx.shape)\n",
    "\n",
    "plt.contourf(xx, yy, survival_predictions, cmap=sns_cmap)\n",
    "plt.xlabel('fare')\n",
    "plt.ylabel('survived')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now create the same plot, but with the original data overlaid on top. Your code should be exactly like the cell above, but should use the `sns.scatterplot` command similar to what you saw in the lecture 5 notebook.\n",
    "\n",
    "Note: If you want to adjust the x and y axes, you can use `plt.xlim` and `plt.ylim`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "# this will make the decision boundary plot look nicer\n",
    "\n",
    "sns_cmap = ListedColormap(np.array(sns.color_palette())[0:2, :])\n",
    "\n",
    "xx, yy = np.meshgrid(np.linspace(0, 550, 20), np.linspace(0, 80, 20))\n",
    "\n",
    "survival_predictions = p2a_model.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "survival_predictions = survival_predictions.reshape(xx.shape)\n",
    "\n",
    "plt.contourf(xx, yy, survival_predictions, cmap=sns_cmap)\n",
    "plt.xlabel(\"fare\")\n",
    "plt.ylabel(\"age\")\n",
    "sns.scatterplot(data=p2a_df,\n",
    "                x=\"fare\",\n",
    "                y=\"age\",\n",
    "                hue=\"survived\",\n",
    "                s=20,\n",
    "                edgecolor=\"black\",\n",
    "                linewidth=0.5)\n",
    "plt.xlim([0, 550])\n",
    "plt.ylim([0, 80])\n",
    ";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### P2C: Assessing Our Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute the accuracy of your classifier. You should get a value near 65%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "survival_predictions = p2a_model.predict(p2a_df[[\"fare\", \"age\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "1      1\n",
       "2      1\n",
       "3      1\n",
       "4      0\n",
       "      ..\n",
       "885    0\n",
       "886    0\n",
       "887    1\n",
       "889    1\n",
       "890    0\n",
       "Name: survived, Length: 714, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p2a_df[\"survived\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6568627450980392"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy = sum(\n",
    "    survival_predictions == p2a_df['survived']) / len(survival_predictions)\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 3: Multidimensional Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this part, we'll use the `age`, `fare`, `pclass`, and `sex` of each passenger to build a logistic regression model. We'll also practice using a training set that is separate from the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "p3_df = df[[\"age\", \"fare\", \"pclass\", \"sex\", \"survived\"]].copy()\n",
    "p3_df = p3_df.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `pclass` indicates the class of the passenger, and can be 1, 2, or 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    355\n",
       "1    186\n",
       "2    173\n",
       "Name: pclass, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p3_df['pclass'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`sex` refers to whether the passenger was male or female."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      453\n",
       "female    261\n",
       "Name: sex, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p3_df['sex'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To use the sex, we'll need to replace \"male\" with 0, and \"female\" with 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "p3_df['sex'] = p3_df['sex'].replace(\"male\", 0)\n",
    "p3_df['sex'] = p3_df['sex'].replace(\"female\", 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>fare</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age     fare  pclass  sex  survived\n",
       "0  22.0   7.2500       3    0         0\n",
       "1  38.0  71.2833       1    1         1\n",
       "2  26.0   7.9250       3    1         1\n",
       "3  35.0  53.1000       1    1         1\n",
       "4  35.0   8.0500       3    0         0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p3_df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### P3A: Splitting Our Data into a Training and a Test Set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before splitting our data, let's shuffle it to avoid any issues when splitting into a training and test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(\n",
    "    100)  # sets the random seed for consistency every time you run the notebook\n",
    "p3_df = p3_df.sample(frac=1)  # shuffles the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 714 people in our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "714"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(p3_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split the data into a training set of 600 people, and a test set of 114. *Hint:* Use `np.split`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "600"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p3_train, p3_test = np.split(p3_df, [600])\n",
    "len(p3_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### P3B: Training and Assessing a 4 Feature Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fit a model `p3b_model` that uses all four of our features to predict survival. Make sure to ONLY use the training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p3b_model = LogisticRegression()\n",
    "p3b_model.fit(p3_train[['fare', 'age', 'pclass', 'sex']], p3_train['survived'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the cell below, compute the training set accuracy. You should get something close to 80%. This is great! This is significantly better than what we got with just the fare and age."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.13700252e-03, -3.59973150e-02, -1.14443309e+00,\n",
       "         2.40443620e+00]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p3b_model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.795"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_predictions = p3b_model.predict(\n",
    "    p3_train[['fare', 'age', 'pclass', 'sex']])\n",
    "training_accuracy = sum(training_predictions == p3_train['survived']) / len(\n",
    "    p3_train['survived'])\n",
    "training_accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now compute the accuracy on the test set. You'll see that it is very close to your training accuracy. This is amazing news! It means that our model is not overfitting. Even without ever seeing the test data, our model was able to get around 79% of the passengers correct."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7807017543859649"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_predictions = p3b_model.predict(p3_test[['fare', 'age', 'pclass', 'sex']])\n",
    "test_accuracy = sum(test_predictions == p3_test['survived']) / len(\n",
    "    p3_test['survived'])\n",
    "test_accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### P3C: Going Polynomial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is possible that there is interaction between our terms. For example, the age of a passenger might have an effect that depends on the class of that passenger.\n",
    "\n",
    "To capture these effects, create a new model `p3c_model` that has 16 features, corresponding to a degree 2 polynomial of our original 4 features.\n",
    "\n",
    "Use the sklearn pipeline feature. Make sure to use a StandardScaler like in HW4. That is, your pipeline should have 3 steps: `StandardScaler`, `PolynomialFeatures`, and `LogisticRegression`.\n",
    "\n",
    "For this problem, make sure to set `penalty = 'none'`. This will disable regularization. We'll discuss regularization in lecture 6.\n",
    "\n",
    "**Note:** Because the `PolynomialFeatures` step already creates an intercept, it is slightly better practice to set `fit_intercept = False` in `LogisticRegression`, but this is not necessary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('scale',\n",
       "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
       "                ('poly',\n",
       "                 PolynomialFeatures(degree=2, include_bias=True,\n",
       "                                    interaction_only=False, order='C')),\n",
       "                ('model',\n",
       "                 LogisticRegression(C=1.0, class_weight=None, dual=False,\n",
       "                                    fit_intercept=False, intercept_scaling=1,\n",
       "                                    l1_ratio=None, max_iter=100,\n",
       "                                    multi_class='auto', n_jobs=None,\n",
       "                                    penalty='none', random_state=None,\n",
       "                                    solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                                    warm_start=False))],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p3c_model = Pipeline([('scale', StandardScaler()),\n",
    "                      ('poly', PolynomialFeatures(2)),\n",
    "                      ('model',\n",
    "                       LogisticRegression(fit_intercept=False,\n",
    "                                          penalty='none'))])\n",
    "p3c_model.fit(p3_train[['fare', 'age', 'pclass', 'sex']], p3_train['survived'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute the training accuracy of your model below. It should be around 82%. It's an improvement over what we had before, implying the polynomial features do actually help, at least somewhat."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8316666666666667"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_predictions = p3c_model.predict(\n",
    "    p3_train[['fare', 'age', 'pclass', 'sex']])\n",
    "training_accuracy = sum(training_predictions == p3_train['survived']) / len(\n",
    "    p3_train['survived'])\n",
    "training_accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute the test accuracy of your model below. You'll see it is somewhat lower (77%) than the training accuracy and also slightly lower than when we fit a model without polynomial features. This implies that there is potentially overfitting happening here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7719298245614035"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_predictions = p3c_model.predict(p3_test[['fare', 'age', 'pclass', 'sex']])\n",
    "test_accuracy = sum(test_predictions == p3_test['survived']) / len(\n",
    "    p3_test['survived'])\n",
    "test_accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One quick note: We used the test set twice in this homework, once for p3b_model, and once for p3c_model. **In the real world, it would be absolutely inappropriate to pick p3c_model over p3b_model because of its test set performance.** You should never use the test set to pick a model, as this means you are effectively fitting your model to the test data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this homework, you built a classifier that was able to predict deaths on the Titanic with fairly decent accuracy. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Optional P3D: High Degee Polynomial Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Optionally, try fitting a degree 5 polynomial just like in p3c_model. Then compute the training and test accuracy. You should see that the training error is lower, but the test error is higher. This is a warning that we are overfitting. Note: You may need to set the `max_iter` in the `LogisticRegression` object to a larger number, e.g. 10000 or even 100000.\n",
    "\n",
    "Again, make sure to set `penalty = 'none'`.\n",
    "\n",
    "In the next lecture we will talk about regularization in the context of Logistic Regression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Programing\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:940: ConvergenceWarning:\n",
      "\n",
      "lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of f AND g EVALUATIONS EXCEEDS LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('scale',\n",
       "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
       "                ('poly',\n",
       "                 PolynomialFeatures(degree=5, include_bias=True,\n",
       "                                    interaction_only=False, order='C')),\n",
       "                ('model',\n",
       "                 LogisticRegression(C=1.0, class_weight=None, dual=False,\n",
       "                                    fit_intercept=False, intercept_scaling=1,\n",
       "                                    l1_ratio=None, max_iter=100000,\n",
       "                                    multi_class='auto', n_jobs=None,\n",
       "                                    penalty='none', random_state=None,\n",
       "                                    solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                                    warm_start=False))],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p3d_model = Pipeline([('scale', StandardScaler()),\n",
    "                      ('poly', PolynomialFeatures(5)),\n",
    "                      ('model',\n",
    "                       LogisticRegression(fit_intercept=False,\n",
    "                                          penalty='none',\n",
    "                                          max_iter=100000))])\n",
    "p3d_model.fit(p3_train[['fare', 'age', 'pclass', 'sex']], p3_train['survived'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now create a plot showing the jittered data overlaid with the probability of survival predicted by your model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8566666666666667"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_predictions = p3d_model.predict(\n",
    "    p3_train[['fare', 'age', 'pclass', 'sex']])\n",
    "training_accuracy = sum(training_predictions == p3_train['survived']) / len(\n",
    "    p3_train['survived'])\n",
    "training_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8245614035087719"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_predictions = p3d_model.predict(p3_test[['fare', 'age', 'pclass', 'sex']])\n",
    "test_accuracy = sum(test_predictions == p3_test['survived']) / len(\n",
    "    p3_test['survived'])\n",
    "test_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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